My application is to detect small foreign objects (please see attached photo) such as cloth fibers that stick between (shiny) transparent film and white synthetic paper surface.
Can you suggest how to use OpenMV Cam for this application?
Is there any recommended microscope lens for use with OpenMV Cam?
Thank you.

Hi,
This is what the camera sees from a distance of less than 10 millimeters and from an angle of about 45 degrees.
1st object is human eyelashes (3.5 millimeter length) & 2nd object is cloth fiber (1 millimeter length).

For your application you really need adaptive thresholding. We don't have that right now but it's on my giant to do list.

Anyway, in the mean time you can get around the issue with the giant blob on the bottom by filtering out blobs which have a high pixel count.

So, call the find_blobs() method on the image using the color thresholds you figured out. Then, filter out all blobs with more than 500 pixels and less than 100 pixels. This should just tell you about blobs that are effectively dirt.

Also, you may wish to make the lighting flatter. Finally, use histeq() to pump the contrast up the image to make setting the color thresholds easier.

Hi,
Attached is the result of camera detection of 4 objects.
The camera can detect 1.5 mm dirt (fb1), with and without additional lighting.
But it can not detect 1 mm cloth fiber (fb2) and 3.5 mm human eyelashes (fb3).
Can you suggest another method to detect fb2 and fb3?
How to reduce the influence of environmental lighting on detection?
Thanks.

Hi, you really need to improve the lighting in the scene. I can see the cameras shadow in the image...

If you're serious about this application you need to mount the camera to a jig where it's stable, then add flood lights around it that evenly illuminate the scene.

An evenly illuminated scene should not have the shadow ring you see around the edge of the image. Nor the cameras shadow in the image. You have to fix these things because the camera is effectively just looking for dark spots which shadows will look like.

If you look at professional computer vision systems like a pick and place machine you'll notice they use giant high powered flood lights to remove all shadows. This is want you need to do. Note that the lighting should not cast more shadows, so, it needs to be diffused.

Hi, the next version of the firmware will feature adaptive thresholding to deal with the issue you're having. This will pick out the hair and just that very easily without any effort or additional lighting control.

Let me know if you need the feature now and I can make a firmware image available to you.

Hi, I've attached the firmware and an example script. The new method solves your problems for the first set of images you presented to me. As for the second set... no method will clean that much noise up in the image. Adaptive threshold just solves the problem of making light shadows not an issue.

1) The algorithm decides to make the output pixel white or not based on this logic:

pixel_area_average(x,y) - offset < pixel_value(x,y)

Where the area average is the average of all pixels around that pixel. Basically, if the average minus the offset is less than the value of the pixel then the output is a 1. So, changing the offset value just adjusts how the output is generated.

In general, the algorithm tends to mark areas where there's a sudden change. The offset value allows you to control exactly how much of a sudden change you threshold.

Hi,
Below is my question:
1) What is the effect if the offset value is increased or decreased?
By using this new method:
1) Does the object color/grayscale threshold value still have to be defined?
2) Does img.mean() has a returned object, so draw_rectangle() can be used on found object?

You can do VGA in grayscale. But, not in RGB565. This is because for RGB565 (for VGA) mode we actually switch to operating in bayer pattern mode and jpeg compress the images to get video output. We have to do this because we don't have the RAM for a full VGA RGB565 image. Anyway, VGA for RGB565 is just for taking jpeg compressed images and nothing else.

Regarding the thresholds constant, what exactly does each value in the threshold variable do?

Im trying to do the same thing but I dont understand what each value is representing?

Ex: thresholds = (70, 100, -128, 127, -128, 127)

I saw this example code below, which makes sense. Which finds extremely bright areas. But why use more than 2 values?
# The below grayscale threshold is set to only find extremely bright white areas.
thresholds = (245, 255)